1. Field of the Invention
The present invention relates to camera self-calibration technology for calculating a focal length, a position, and a direction of a camera from the position of a correspondence point between images, and more particularly, to a method for estimating a focal length to automatically calibrate a camera using directional coincidence of 3 dimension (D) coordinate systems having geometrically meaningful values in a 3D Euclidean space as a restriction condition.
2. Description of the Related Art
As a method for automatically calibrating a camera of a fixed local length, Sturm has derived Equation for calculating a focal length using a closed form from singular value decomposition (SVD) of a fundamental matrix that can be obtained from a position of a correspondence point between images.
The Equation derived by Sturm has been calculated using an absolute quadric, which is an imaginary geometric entity defined in a complex number space.
Since the imaginary geometric entity has been used, any measurable reference that can guarantee accuracy of calculation does not exist in the case where an error is generated to the position of a correspondence point. Accordingly, the method by Sturm provides results very sensitive to an image noise.
Meanwhile, a method for defining a cost function for automatic calibration and calculating a focal length that minimizes the cost function through non-linear optimization has been proposed.
Mendonca and Cipolla have defined the cost function by applying a restriction condition that two initial singular values of an essential matrix should be the same at an accurate focal length. Also, they have calculated a focal length that minimizes the cost function through non-linear optimization.
At this point, an initial value of a focal length for the non-linear optimization can be estimated by Sturm's method. This non-linear optimization method can obtain an accurate result compared to obtaining a solution of a closed form.
However, since a restriction condition applied to an essential matrix is also derived from an absolute quadric, which is an imaginary geometric entity, there does not exist a geometrical measurement reference that can guarantee the reliability of a calculating result. In the case where an image noise exists simply because a numerical value of a cost function is minimized, a reliability problem still exists.